Lyrics Information Processing: Analysis, Generation, and Applications

Kento Watanabe and Masataka Goto
National Institute of Advanced Industrial Science and Technology (AIST){kento.watanabe, m.goto}@aist.go.jp

Abstract
In this paper we propose lyrics information
processing (LIP) as a research field for technologies focusing on lyrics text, which has
both linguistic and musical characteristics.
This field could bridge the natural language
processing field and the music information retrieval field, leverage technologies developedin those fields, and bring challenges that encourage the development of new technologies.
We introduce three main approaches in LIP, 1)
lyrics analysis, 2) lyrics generation and writing
support, and 3) lyrics-centered applications,and briefly discuss their importance, current
approaches, and limitations.

1

Introduction

For songs that are musical pieces with singing
voices, lyrics text is one of key factors that makelisteners feel songs are attractive because it delivers
messages and expresses emotion. Since the lyrics
text plays an important role in music listening andcreation, some studies in the music information
retrieval (MIR) community have already focusedon it, but not as many as studies that have focusedon musical audio signals and musical scores. Similarly, in the natural language processing (NLP)
community there have not been many studies focusing on lyrics text, and most NLP methods assume prose text, not lyrics text. Since lyrics text
is a series of words, some NLP methods could beapplied to it successfully, but NLP methods are notalways effective for lyrics text because the naturesof lyrics and prose texts are different as describedin Section 2.
We therefore propose to refer to a broad range oflyrics-related studies as lyrics information processing (LIP), which could also be considered music
information processing for lyrics texts. LIP sharessome core technologies with NLP and MIR, andresearch and development of LIP could contributeto the MIR and NLP communities as follows:(1) Academic contributions: Since lyrics are an
important aspect of music information, LIP could
broaden the scope of MIR and complement it.
Since lyrics are a difficult form of natural language,
LIP could provide challenging issues that are notaddressed by existing NLP technologies. The nature of lyrics (e.g., style, structure, and semantics)
could also be investigated by automatically analyzing and generating lyrics text data.
(2) Industrial contributions: LIP could open up
practical applications that are useful for listeners
and creators, such as lyrics classification, lyrics exploration, lyrics summarization, and lyrics writing
support.
This paper gives an overview of LIP by categorizing lyrics-related studies into three main approaches: lyrics analysis, lyrics generation, andapplications. Since the concept of LIP is broadand still emerging, we hope that this paper could
stimulate further development of LIP.

2

Lyrics analysis

Because lyrics and poetry1 have unique linguistic
properties, NLP technologies for prose text are notalways effective enough to analyze lyrics text. In
this section we introduce studies of lyrics analysis
regarding the structure and semantics of lyrics andits relationship with audio.
2.1

Lyrics structure analysis

Rhyme scheme identification: The rhyme
scheme is the pattern of rhymes at the end of lyric
lines. It is usually represented by using a series ofletters corresponding to lines, in which repeated letters indicate rhymed lines. In the following example (RWC-MDB-P-2001 No.83 (Goto et al., 2002)),
1

Lyrics and poetry are different types of text because lyrics
are assumed to be sung along with music. However, somelinguistic properties of lyrics and poetry overlap.

two consecutive lines having the same letter rhyme:
A: Race the clock I got to score
A: Work or play Back for more
B: Only true believers rise to the top,
B: Licking the cream of the cropThis rhyme scheme is “AABB” and is called Couplet2 . Since typical prose text analyzers suchas part-of-speech analyzers and grammar tree
parsers cannot analyze rhyme schemes, some studies addressed the rhyme scheme identification task.
Given a few lines of lyrics (paragraph or stanza)
as the input, their rhyme scheme (ABC label sequence) is estimated. For example, Reddy andKnight (2011) and Addanki and Wu (2013) estimated the rhyme scheme by using languageindependent unsupervised methods (e.g., hidden
Markov models) that do not depend on morphological and phonological properties.
Lyrics segmentation: While the rhyme scheme isa line-by-line repetitive structure, lyrics also have aparagraph-by-paragraph structure like verse-bridgechorus. Paragraphs are usually separated by a blankline, but in some lyrics they are not. Some studies
therefore tackled the lyrics segmentation task inwhich the boundaries between paragraphs are estimated from lyrics without blank lines (Watanabe
et al., 2016; Fell et al., 2018). They showed that the
self-similarity matrix, which is often used in music
structure analysis of audio signals in the MIR community, can be applied to lyrics text to improve theperformance of lyrics segmentation. This is a good
example of integrating NLP and MIR methods toaccomplish a LIP task.
Verse-bridge-chorus labeling: Given paragraphsof lyrics, assigning a structural label such as verse,
bridge, and chorus to each paragraph is also an
important task. Simple rule-based methods suchas a method of grouping paragraphs with the samelabel (Baratè et al., 2013) and a method of labeling
each paragraph (Mahedero et al., 2005) have been
proposed. Since a sufficient amount of lyrics data
annotated with structural labels is still lacking formachine-learning approaches, there is much room
for improvement.
2.2

Lyrics semantic analysis

Emotional expressions, topics, and stories in lyrics
are factors that have a great influence on listeners’ emotions. Since lyrics tend to be constrained
2

There are various rhyme schemes, such as ABAB (Alternate Rhyme), ABABBCBC (Ballade), AAAAA (Monorhyme),
AAABBB (Triplet), and ABBA (Enclosed Rhyme).

by melody lines and have a limited length, a typical way of expressing messages in lyrics is different from the way they are expressed in prose
text. Lyrics messages are often emotional, inspiring, concise, and (intentionally) obscure. Even if
detailed moods, topics, and stories are not explicitly described in lyrics, listeners can enjoy guessing
or inferring them. Some studies have already analyzed such semantic factors behind lyrics text.
Mood estimation: Supervised learning-based
methods estimating the mood or emotion of lyrics
have been developed (Wang et al., 2011; Hu andDownie, 2010; Delbouys et al., 2018) and are basedon a word dictionary in which valence and arousal
values (Russell, 2003) are annotated (Bradley andLang, 1999; Warriner et al., 2013). Since a lot ofmood estimation methods for audio signals havebeen proposed in the MIR community, it would bepossible to develop mood estimation based on both
lyrics text and audio. In the future, unsupervised
methods and support for low-resource languages
are expected to be developed because supervised
learning-based methods require training data ofannotated lyrics, which are language-dependent.
Topic modeling: For lyrics topic modeling, unsupervised methods such as latent Dirichlet allocation(LDA), non-negative matrix factorization, and theirextensions are often used (Kleedorfer et al., 2008;
Sasaki et al., 2014; Tsukuda et al., 2017). Unlike mood estimation methods, these methods do
not require training data with valence and arousal
values, which results in the advantage of easily
preparing training data for different languages. The
obtained word topics (clusters) are further used asclues for classification tasks or used in visualization functions for music exploration. It is, however,difficult to appropriately evaluate the accuracy of
topics obtained by unsupervised learning. A previous study tackled this difficulty by evaluating thecorrelation between estimated topics clusters andhuman-annotated ones (Sterckx et al., 2014).
Storyline modeling: Lyric writers consider
themes and stories when writing lyrics. For theverse-bridge-chorus structure of lyrics, an example
of a storyline represented as a topic transition is
introduction (verse) → past event (bridge) → emotional message (chorus). Watanabe et al. (2018b)
proposed an extended hidden Markov model tolearn this topic transition structure from lyrics data
without supervision. Their model learned topic
transitions that are often found in love songs, hip-hop songs, and so on even if they are not explicitly
given.
2.3

Analysis of the relationship between
lyrics text and music audio

A clear difference between lyrics and poetry is thepresence or absence of accompanying music. Since
investigating the relationship and synchronization
between lyrics and music audio is an important
topic of research, there have been various related
studies that deal with the relationship between syllable stress and pitch (Nichols et al., 2009), the relationship between words and chords (Greer et al.,2019), the relationship between rests in melody andboundaries of words, lines, and paragraphs (Watanabe et al., 2018a), and lyrics-to-audio alignment(Kan et al., 2008; Fujihara et al., 2011; Mauchet al., 2012; Chien et al., 2016; Chang and Lee,2017; Stoller et al., 2019; Gupta et al., 2019).

3

Lyrics generation and writing support

As natural language generation (NLG) has been
actively researched, automatic lyrics generation
is becoming a popular topic of research. NLG
technologies have been greatly improved in performance by deep neural networks (DNNs) and are
utilized in applications such as machine translation and dialogue systems. Generating poetry andnovels has also been developed, though generating
creative text is challenging. Generating lyrics is
also challenging and has further technical difficulties caused by lyrics-specific musical constraints
such as melodies and rhymes. In this section we
introduce studies of lyrics generation as well as
writing support systems that utilize lyrics generation methods.
3.1

Automatic lyrics generation

Rhyme-scheme-conditioned lyrics generation:
Since lyrics and poetry often have rhyme schemes
as introduced in Section 2.1, some studies haveaddressed the task of generating lyrics and poetrythat satisfy constraints of a rhyme scheme (Barbieri
et al., 2012; Hopkins and Kiela, 2017). In automatically generating lyrics, most methods use language
models such as n-grams and recurrent neural networks as well as word sequence search based on
the Markov process. To deal with the constraints,
several extended word-sequence search methods
have been proposed, such as those using the strong
constraint that words that do not satisfy the rhyme

scheme are discarded during word sequence searchand the weak constraint that the score is calculated
based on how well the given rhyme scheme is satisfied.
Melody-conditioned lyrics generation: Although most studies of automatic lyrics generation
have generated lyrics using only text data without considering musical audio signals and musical scores, some studies have addressed the taskof generating fluent lyrics that are singable whena melody (a sequence of musical notes) is given(Lu et al., 2019). Watanabe et al. (2018a) confirmed that the frequency of word/line/paragraphboundaries depends on the duration of rests andproposed an advanced lyrics language model thattakes advantage of this dependency. Their method
can generate segmented lyrics that are singablefor the verse-bridge-chorus structure of the input
melody. It, however, requires training data in which
lyrics syllables and melody notes are aligned. Suchdata could be easily created if technologies suchas the above-mentioned lyrics-to-audio alignment,
lyrics recognition (Hosoya et al., 2005; Dabike andBarker, 2019; Suzuki et al., 2019), and melody notetranscription (Yang et al., 2017; Román et al., 2018;
Nishikimi et al., 2019) could mature in the future.
Automatic generation of structured lyrics: Most
lyrics generation systems can generate only one
paragraph of lyrics, though lyrics have some paragraphs in general. This is because language models for lyrics did not explicitly capture the consistency of topics and relations between paragraphs.
Watanabe et al. (2014) have proposed a probabilistic model that captures topic transitions between
paragraphs to generate lyrics having the storyline.
Fan et al. (2019) have proposed a lyrics generation method using the long short-term memory language model that captures the hierarchical structureof words, lines, and paragraphs to leverage the dependency of long word sequences. Although thesestudies have made it possible to generate lyrics thatare almost consistent in topic, it is still difficult togenerate lyrics that are consistent in meaning.
Ghostwriting: Ghostwriting is a task of generating new lyrics that follow the style (e.g., rhyme
scheme, phrasing, content, and the number ofwords per line) of a given artist. Potash et al.
(2015) proposed a rap-lyrics generation method
based on data-driven learning of the artist’s styleusing a DNN-based language model trained withthe artist’s lyrics corpus.

3.2

Writing support system with automatic
lyrics generation

Automatic lyrics generation makes it possible todevelop systems that support lyrics writing. It is
not easy for novices to write lyrics by thinking ofappropriate words and phrases while consideringvarious constraints and properties. Since candidate
word sequences satisfying various constraints canbe generated automatically, it is useful to show
them to lyric writers to support their creative activities. Some studies have developed interactive
systems that support lyrics writing by repeatedly
recommending candidate word sequences that satisfy constraint parameters input by the user.
pâtissier (Abe and Ito, 2012) is an interface thatallows the user to specify syllable counts, syllable
stress, and vowels, and generates candidate sentences that satisfy them. DeepBeat (Malmi et al.,2016) is an interface that generates and suggests
next-line candidates that rhyme with a line enteredby the user. LyriSys (Watanabe et al., 2017) and
Co-PoeTryMe (Oliveira et al., 2019) are interfacesthat allow the user to specify song structure andsyllable counts, select or enter topics and keywords
for each paragraph, and make the system generate
candidate lyrics that satisfy them. These interfaces
also allow the user to manually edit the generated
lyrics.

4

Applications for a collection of lyrics

Like NLP technologies, LIP technologies are useful
in developing various applications, such as classification, exploration, and summarization, for a large
collection of lyrics data.
4.1

Lyrics classification

Given a collection of lyrics, it is useful to classify
and visualize them. Genre classification for lyrics
is a popular approach that has already been studied (Mayer et al., 2008; Mayer and Rauber, 2011;
Tsaptsinos, 2017). Some characteristics peculiar to
lyrics (e.g., rhyme scheme, structure, meaning, andrelationship with audio) have been used as features
to train a supervised classifier.
4.2

Lyrics exploration

If a user wants to see the lyrics of a song the user
knows, simple text-based lyrics retrieval is enough,but if a user wants to encounter unfamiliar but interesting lyrics, a content-based music exploration
system focusing on lyrics is necessary. Baur et al.

(2010), Sasaki et al. (2014), and Tsukuda et al.
(2017) have developed such exploration systems
that visualize topics of lyrics and similar artists byanalyzing the content of lyrics using LDA, selforganizing maps, and so on. Query-by-Blending(Watanabe and Goto, 2019) is a music exploration
system that enables a user to give flexible queries
related to lyrics, audio signals, and artist tags byusing a unified latent vector space with these threedifferent modalities embedded.
4.3

Lyrics summarization

In browsing a collection of lyrics, a short summary of lyrics of each song helps navigate quickly.
Fell et al. (2019) improved the performance of the
lyrics summarization task by combining a general
document summarization method with an audio
thumbnailing method. Summarization more advanced than simply extracting lines, such as phrase
paraphrasing and compression, requires development of advanced technologies for lyrics semantic
analysis.

5

Conclusion

In this paper we have provided an overview oflyrics information processing (LIP) and have described examples of studies from the viewpoint oflyrics analysis, lyrics generation, and applications.
Those examples are just excerpts taken from a variety of previous studies and possible future technologies. For example, the limited space does not allow
us to discuss the relationship with singing information processing (SIP) (Goto et al., 2010; Goto,
2014; Humphrey et al., 2019), though we mentioned the lyric-to-audio alignment. Since lyrics
are sung by singers, there are many possibilities
to investigate the relationship between lyrics andthe corresponding singing expressions and styles.
Lyrics are thus linguistic, musical, and singablefrom the NLP, MIR, and SIP viewpoints, respectively. Since LIP is an emerging interdisciplinary
research field that could be related to various technologies and disciplines such as natural language
processing, music information retrieval, machinelearning, human-computer interaction, visualization, signal processing, linguistics, and musicology,
we expect research on LIP to progress in coming
years from a diverse viewpoint by attracting more
attention due to its importance and potential.

6

Acknowledgments

This work was supported in part by JST ACCEL
Grant Number JPMJAC1602, and JSPS KAKENHI
Grant Number 20K19878 Japan.

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